/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.zhaohg.spark.examples.mllib;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.feature.ChiSqSelector;
import org.apache.spark.mllib.feature.ChiSqSelectorModel;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;

// $example on$
// $example off$

public class JavaChiSqSelectorExample {
    public static void main(String[] args) {
        
        SparkConf conf = new SparkConf().setAppName("JavaChiSqSelectorExample");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        
        // $example on$
        JavaRDD<LabeledPoint> points = MLUtils.loadLibSVMFile(jsc.sc(),
                "data/mllib/sample_libsvm_data.txt").toJavaRDD().cache();
        
        // Discretize data in 16 equal bins since ChiSqSelector requires categorical features
        // Although features are doubles, the ChiSqSelector treats each unique value as a category
        JavaRDD<LabeledPoint> discretizedData = points.map(lp -> {
                    final double[] discretizedFeatures = new double[lp.features().size()];
                    for (int i = 0; i < lp.features().size(); ++i) {
                        discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16);
                    }
                    return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures));
                }
        );
        
        // Create ChiSqSelector that will select top 50 of 692 features
        ChiSqSelector selector = new ChiSqSelector(50);
        // Create ChiSqSelector model (selecting features)
        final ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd());
        // Filter the top 50 features from each feature vector
        JavaRDD<LabeledPoint> filteredData = discretizedData.map(
                lp -> new LabeledPoint(lp.label(), transformer.transform(lp.features()))
        );
        // $example off$
        
        System.out.println("filtered data: ");
        filteredData.foreach(labeledPoint -> System.out.println(labeledPoint.toString()));
        
        jsc.stop();
    }
}
